## Rows: 29521 Columns: 12
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): State and area
## dbl (11): Count, FIPS Code, Year, Month, Population, LaborForce, LaborForce_...
##
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## New names:
## Rows: 37 Columns: 21
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): AreaAll, winner_party, president_party, H_incumbent_party
## dbl (16): ...1, year, R_seats, D_seats, Other_seats, total_votes, R_votes, D...
## lgl (1): H_incumbent_party_winner
##
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## New names:
## Rows: 298 Columns: 9
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): DATE, year_qt
## dbl (7): ...1, ...2, UNRATE, year, quarter_yr, quarter_cycle, unrate_qt_total
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## New names:
## Rows: 254 Columns: 9
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): date, year_qt
## dbl (7): ...1, year, quarter_yr, quarter_cycle, DSPIC_qt, DSPIC_change_qt, D...
##
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## New names:
## Rows: 761 Columns: 7
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (3): DSPIC96, quarter_yr, quarter_cycle
## lgl (3): ...5, ...6, ...7
## date (1): date
##
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## Rows: 16067 Columns: 31
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (16): Office, State, Area, RepCandidate, RepStatus, DemCandidate, DemSta...
## dbl (14): raceYear, RepVotes, DemVotes, ThirdVotes, OtherVotes, PluralityVot...
## lgl (1): CensusPop
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.In my previous blogs, I have focused solely at national variables and conditions which may affect our election outcomes. Things like GDP, unemployment, polling, etc. are very helpful tools but we also have the option to look at elections from a much closer, district level approach. On the district level, many experts watch and weigh in on important races. Organizations such as Cook Political Report and Sabato’s Crystal Ball take into account local conditions and determine ratings on an individual district level basis. These ratings are usually considerably accurate, with over 96% of House of Representative elections being accurately predicted between 1998 and 2016 (Silver, 2022). In this blog post, I will evaluate my existing model with the inclusion of incumbency and expert rating data. I will also create another model to visualize actual vote share to various expert predictions for a particular year, I will chose 2018.
## Reading layer `districts114' from data source
## `/private/var/folders/mv/pwxqfd411cz090w_kkkl3mn00000gn/T/RtmpViYfTL/districtShapes/districts114.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 436 features and 15 fields (with 1 geometry empty)
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -179.1473 ymin: 18.91383 xmax: 179.7785 ymax: 71.35256
## Geodetic CRS: NAD83